对谁而言是可步行的？使用多模态深度学习捕捉步行感知中的主观变异性
Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning
- School of Civil and Environmental Engineering, University of New South Wales (UNSW)（新南威尔士大学土木与环境工程学院）
- Research Centre for Integrated Transport Innovation (rCITI)（综合交通创新研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究构建含29870条评分的步行性数据集，提出用户条件多模态深度学习框架，发现人行道图像评分更高，模型一致性提升65%，助力构建更包容的行人环境评估模型。
AI中文摘要：
个体对步行性的视觉感知存在显著差异，这反映了个人特征、经历和偏好的不同。然而，现有研究往往将这些多样化的判断简化为聚合评分，隐含假设感知是统一的，且通常依赖车载街景图像，这些图像无法反映行人的视觉体验。本文介绍了一个包含1196名受访者的29870条步行性评分的数据集，将澳大利亚城市、郊区和区域环境中的人行道视角图像与个体评分者属性关联起来，并提出了首个用于步行性感知的用户条件多模态深度学习框架，该框架将视觉特征与受访者层面的表示进行融合。一项视角对比研究显示，人行道视角图像获得的步行性评分显著高于匹配的街景图像，这表明图像来源是感知调查中的一个重要设计决策。与仅基于图像的基线模型相比，该用户条件模型与观测评分的等级一致性提升了65%（二次加权kappa值为0.47，而基线为0.29），证明了评估环境的主体除了图像内容之外还具有预测性指示作用。这些发现支持从聚合的、与观察者无关的步行性评分转向能够代表多样化用户的模型，从而实现对行人环境更具包容性的评估。
英文摘要：
Visual perception of walkability varies substantially across individuals, reflecting differences in personal characteristics, experiences, and preferences. Existing studies, however, often reduce these diverse judgements to aggregated scores, implicitly assuming uniform perception, and commonly rely on vehicle-mounted street-view imagery that does not reflect the pedestrian's visual experience. This paper introduces a dataset of 29,870 walkability ratings from 1,196 respondents, linking sidewalk-view imagery across urban, suburban, and regional Australian environments with individual rater attributes, and proposes the first user-conditioned multimodal deep learning framework for walkability perception, fusing visual features with respondent-level representations. A viewpoint-comparison study shows that sidewalk-view images receive significantly higher walkability ratings than matched street-view images, indicating that imagery source is a substantive design decision in perception surveys. The user-conditioned model improves rank agreement with observed ratings by 65% over an image-only baseline (quadratic weighted kappa 0.47 vs. 0.29), demonstrating that who is evaluating an environment carries predictive indication beyond image content alone. These findings support moving from aggregated, observer-independent walkability scores toward models that represent diverse users, enabling more inclusive assessment of pedestrian environments.
